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Spatio-temporal variations of rainfall erosivity, correlation of climatic indices and influence on human activities in the Huaihe River Basin, China

分水岭 环境科学 流域 构造盆地 降水 气候学 水文学(农业) 腐蚀 自然地理学 地理 地质学 气象学 地貌学 地图学 机器学习 岩土工程 计算机科学
作者
Chong Wei,Xiaohua Dong,Dan Yu,Te Zhang,Wenyi Zhao,Yaoming Ma,Zhongbo Su
出处
期刊:Catena [Elsevier BV]
卷期号:217: 106486-106486 被引量:19
标识
DOI:10.1016/j.catena.2022.106486
摘要

Rainfall erosivity (RE) is an important factor in the soil erosion process, which cannot be altered by human intervention alone. The Huaihe River Basin (HRB) is a large agricultural watershed, suffering severe water erosion, investigating the spatio-temporal variation of RE is essential for local soil erosion prevention. The linear regression, Yue-Pilon method, and the Hurst exponent were used in analyzing the spatio-temporal variation within the HRB during 1960–2018. The slip correlation analysis and F-test were applied to obtain the correlation between climatic indices [the Arctic Oscillation (AO), the Southern Oscillation (SOI), the North Pacific Index (NPI), and the Niño 4SST index (SST)] and RE in the HRB. Results revealed that the annual average RE were 4280, 5061, 4068, 4886, and 4089 MJ mm hm−2 h−1 within the HRB, upper-HRB, middle-HRB, lower-HRB, and the Yishusi River Watershed, respectively. The annual average RE will increase within the upper-HRB and lower-HRB and decrease within the middle-HRB and the Yishusi River Watershed in the future. The seasonal average RE was ranked as summer > autumn = spring > winter in the HRB. The spatiotemporal difference was significant in the HRB, and different sub-regions exhibited a different trend in seasonal RE, except for the winter RE that increased significantly in all sub-regions (p < 0.05). The highest monthly RE occurred in July, with RE during May–September accounting for approximately 87% of the annual RE in the HRB. The AO and NPI had significant correlations with RE (p < 0.05) both on the annual and monthly scales with different lag times. The monthly AO, SOI, SST, and NPI were significantly correlated with RE in the long term with different lag times in different months (p < 0.05). These findings could provide potential predictive factors for RE prediction and help prevent soil erosion within the HRB.

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